This has code for visualising what is happening with a multiple regression. To run this yourself out of the Markdown, you’ll need to install the package plotly, but the html should work fine for you even if you don’t.

Tutorial 11 dataset visualisation

OCM model (no interaction)

First, let’s visualise the model from where overall policy is predicted by both culture and move, but no interaction effect between the two predictors is included in the model.

## 
## Call:
## lm(formula = overall ~ culture + move, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.044  -8.363   0.269   8.975  31.706 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -27.77263    5.65001  -4.916 1.68e-06 ***
## culture       0.59301    0.07245   8.185 1.83e-14 ***
## move          0.89113    0.05309  16.786  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.47 on 231 degrees of freedom
## Multiple R-squared:  0.5942, Adjusted R-squared:  0.5907 
## F-statistic: 169.1 on 2 and 231 DF,  p-value: < 2.2e-16

OCMi model (with interaction)

We can also visualise the model where overall policy is predicted by both culture and move, now with an interaction effect included.

## 
## Call:
## lm(formula = overall ~ culture * move, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.866  -8.178  -0.139   8.728  30.069 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.624374  13.716776   1.212 0.226766    
## culture      -0.046725   0.194300  -0.240 0.810172    
## move         -0.171955   0.305168  -0.563 0.573660    
## culture:move  0.015363   0.004346   3.535 0.000493 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.15 on 230 degrees of freedom
## Multiple R-squared:  0.6151, Adjusted R-squared:  0.6101 
## F-statistic: 122.5 on 3 and 230 DF,  p-value: < 2.2e-16